Akbary et al. (2026) Scale-dependent biases in Alpine sub-daily areal precipitation extremes: added value of convection permitting models

Identification

• Journal: Hydrology and earth system sciences
• Year: 2026
• Date: 2026-06-30
• Authors: Rashid Akbary, Eleonora Dallan, Paul C. Astagneau, Raul Roger Wood, Francesco Marra, Manuela I. Brunner, Marco Borga
• DOI: 10.5194/hess-30-4117-2026

Research Groups

• Department of Land Environment Agriculture and Forestry, University of Padova, Italy
• WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
• Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
• Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
• Department of Geosciences, University of Padova, Padova, Italy
• Research Center on Climate Change Impacts, University of Padova, Rovigo, Italy

Short Summary

This study evaluates the performance of convection-permitting models (CPMs) in representing areal precipitation extremes over complex terrain in Switzerland. The results show that CPMs reproduce the spatial organization and intensity of extreme precipitation return levels more realistically than regional climate models (RCMs), but with scale-dependent biases.

Objective

• To assess how well CPM simulations represent areal precipitation extremes across durations from 1 to 24 h and spatial scales from ∼10 to 5000 km2.
• To quantify the added value of kilometer-scale modeling in representing short-duration extremes relevant to flash floods and debris flows in small to medium-size Alpine catchments.

Study Configuration

• Spatial Scale: The study covers a range of spatial scales, from approximately 10 to 5000 km2, with areal aggregation using sliding windows and catchment outlines.
• Temporal Scale: The analysis spans durations from 1 h to 24 h, with hourly precipitation fields used as input.

Methodology and Data

• Models used: Convection-permitting models (CPMs) and regional climate models (RCMs), including nine CPM configurations and seven driving RCMs.
• Data sources: CombiPrecip radar-gauge merged product, which provides hourly ground-level precipitation on a 1 km2 grid.

Main Results

• The study finds that CPMs reproduce the spatial organization and intensity of extreme precipitation return levels more realistically than RCMs, but with scale-dependent biases.
• For short-duration extremes (1-3 h), CPM bias in 20-year return levels strongly depends on the spatial scale, shifting from a ∼15% underestimation at native resolution to near-zero bias at ∼400 km2 and to ∼20% overestimation at ∼4000 km2.
• RCMs consistently underestimate 20-year return levels across all spatial scales, with biases ranging from ∼40% underestimation at native resolution (∼144 km2) to ∼10% underestimation at the largest aggregation scales (∼5000 km2).

Contributions

• The study provides new insights into the scale-dependent behaviors of CPMs and RCMs in representing areal precipitation extremes over complex terrain.
• The results highlight the added value of kilometer-scale modeling in representing short-duration extremes relevant to flash floods and debris flows in small to medium-size Alpine catchments.

Funding

• This research was supported by the European Union's Horizon 2020 program under grant agreement No. 730958 (C3S).
• Additional funding was provided by the Swiss National Science Foundation (SNF) under project number PP00P2_170683.
• The study also received support from the Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC.

Citation

@article{Akbary2026Scaledependent,
  author = {Akbary, Rashid and Dallan, Eleonora and Astagneau, Paul C. and Wood, Raul Roger and Marra, Francesco and Brunner, Manuela I. and Borga, Marco},
  title = {Scale-dependent biases in Alpine sub-daily areal precipitation extremes: added value of convection permitting models},
  journal = {Hydrology and earth system sciences},
  year = {2026},
  doi = {10.5194/hess-30-4117-2026},
  url = {https://doi.org/10.5194/hess-30-4117-2026}
}